Distribution of COVID-19 in Indonesia
Fig. 1 shows the distribution of COVID-19 cases in 34 provinces in Indonesia. From the surveillance system report conducted by the acceleration of COVID-19 countermeasures, the confirmed cases of COVID-19 have spread to all provinces. The five provinces with the highest distribution of confirmed cases were DKI Jakarta, Jawa Barat, Jawa Timur, Jawa Tengah, and Sulawesi Selatan. These five provinces also had a relatively high number of deaths. The number of cases cured in the five provinces was also considerably high. The province of Nusa Tenggara Timur (NTT) reported the least number of confirmed cases (n=12). Additionally, no deaths have been reported in NTT. Other provinces that have not reported deaths are Maluku Utara and Jambi.

On May 8, 2020, 13,122 confirmed cases were reported in Indonesia. The cases consisted of 13,091 confirmed cases from 34 provinces and an additional 21 cases that were being verified in the field. The number of reported deaths from 31 provinces was 943 (CFR=7.2%) (Gugus Tugas COVID-19, 2020), which was obtained from a COVID-19 surveillance report conducted in Indonesia. If the surveillance system attributes are examined, namely the completeness of the data, the data only reaches approximately 30% for reporting positive confirmed (35%) and cured cases (39%), and 67.7% for deaths. All confirmed cases (positive, death, or recovered) should have had a reporting date, but many officials did not include a complete report date.

Indonesia has a high mortality rate due to COVID-19. The five provinces in Indonesia with the highest CFR were Banten (11.1%), Jawa Timur (10.7%), DKI Jakarta (8.6%), Jawa Tengah (7%), and Jawa Barat (6.6%). The calculation of mortality was obtained by comparing the number of deaths with the number of confirmed cases (Fig. 2). In addition, DKI Jakarta was the largest contributor to COVID-19 cases in Indonesia and was also the point of entry of SARS-CoV-2 in Indonesia. This was evident from Indonesia’s index case originating from DKI Jakarta. DKI Jakarta has the largest international airport in Indonesia, which is the entrance for foreign tourists. A positive index case occurred after contact with Japanese citizens visiting Indonesia. From DKI Jakarta, the virus spread to the surrounding provinces, especially Jawa Barat. A cluster from Jakarta spread to other areas in the vicinity due to the large number of residents from Jawa Barat who work in Jakarta (DetikNews, 2020). The main coping effort for COVID-19 was the selection of cases. Cases that were identified in the community received treatment. Treatments that have been administered in Indonesia are shown in Fig. 3. Kepulauan Riau, Bali, Aceh, Gorontalo, and DI Yogyakarta were the five provinces that reported the highest cure rates.

The probability of transmission of SARS-CoV-2 is that one positive person can transmit the virus to two to three other people, with an average transmission time of five days (DetikNews, 2020). According to epidemiology theory (Gordis, 2004), this is referred to as the R₀ (R-naught). COVID-19 cases in Indonesia occurred by spreading from metropolitan cities, with the main source of transmission being the Jabodetabek cluster, and then to Surabaya, which is the second largest metropolitan city in Indonesia, as well as the capital of Jawa Timur Province (Erfinanto, 2020). Metropolitan cities are considered sources of employment and education. The largest cluster of SARS-CoV-2 transmission in Jawa Timur was the Hajj Cluster in Sukolilo, Surabaya (Kurniawan, 2020). This is the main cause of the acceleration of the spread of cases in Indonesia (KompasTV, 2020; Mantra, 2000).

The mortality rate (CFR) in Indonesia is relatively high. This can be observed from Indonesia’s CFR (CFR=7.2%), which exceeds that of the world (6.9%) (Kementrian Kesehatan, 2020). The diverse character of Indonesian society was the main reason for the difficulty in preventing and controlling the spread of SARS-CoV-2. Not all Indonesian people adhered to local lockdowns or territorial restrictions, which are the main mechanisms for suppressing the spread of SARS-CoV-2. Additionally, there were many people who were sick and not honest when checking themselves into health facilities. This is precisely the source of SARS-CoV-2 transmission to medical personnel (Liputan 6, 2020).

Phylogenetic Tree Construction
SARS-CoV-2 is thought to have originated from bats that migrated to humans through food in a wholesale market for seafood and wild animals near the city of Wuhan, China (Hui et al., 2020; Li et al., 2020). Bats of the genus Rhinolophus in southern China are known to have many SARS-like CoVs (Sun et al., 2020a). SARS-CoV-2 belongs to the subgenus Sarbecovirus, genus Betacoronavirus, and family Coronaviridae (Gorbalenya et al., 2020). SARS-CoV-2 has characteristics similar to SARS-CoV, including its genome sequence (Xu et al., 2020). SARS-CoV-2 is also similar to the Middle East respiratory syndrome-related coronavirus (MERS-CoV) (Elfiky et al., 2017). This group of viruses is genetically diverse and shows frequent recombination (Sun et al., 2020b; Zhou et al., 2020).

In the distribution process, SARS-CoV-2 may mutate, as SARS-CoV-2 is an RNA virus. The characteristic of RNA viruses is that they mutate up to 1 million times faster than their host (Pachetti et al., 2020). Phylogenetic analysis is a method to explain the evolutionary history and relationships among a group of organisms (Horiike, 2016) and is usually visualized in the form of phylogenetic trees to make it easier to determine the relationship between species. In this study, phylogenetic trees were constructed using the Indonesian SARS-CoV-2 nucleocapsid gene sequences with sequences from isolates sourced from several other countries, as well as sequences of several other types of CoVs collected from the NCBI and GISAID databases. The N-protein is the most abundant protein in coronavirus (Fig. 1), is encoded by a conserved gene, and is used as a molecular marker (Sino Biological, 2020).

Fig. 4 shows that SARS-CoV Tor2 2003 has the closest relationship with Bat-CoV YN2018C 2018. SARS-CoV-2 2020, which is found in various countries, is also closely related to Pangolin-CoV GX-P5E 2017 and Bat-SL-CoV ZC45 2018. The bat coronavirus (BatCoV RaTG13), previously detected in Rhinolophus affinis from Yunnan province, has many similarities with SARS-CoV-2, with an overall genome sequence similarity of 96.2% (Zhou et al., 2020). SARS-CoV-2 2020 is also related to MERS-CoV 2012, Human-CoV HKU1 2004, and Human-CoV OC43 2003; SARS-CoV-2 has a genetic similarity of 50% with MERS-CoV and 79% with SARS-CoV (Rabaan et al., 2020). SARS-CoV and SARS-CoV-2 genome sequences have extremely high levels of nucleotide similarity with only six genomic regions showing differences (Xu et al., 2020).

SARS-CoV-2 from in Indonesia was very closely related to SARS-CoV-2 from other countries. This demonstrated that they had very similar genomes, although there were slight genetic changes during the transmission process. The results of the study by Forster et al. (2020) show that the patient’s travel history determines patterns in the SARS-CoV-2 phylogenetic network. Different phylogeographic patterns can be influenced by different migration histories, founder events, and sample sizes. Additionally, patient treatment and increased patient immunity also affects mutation occurrence. Any decrease in COVID-19 cases and/or CFR is likely caused by increased immunity in the human population and is an epidemiological context of viral mutation (Zhang and Holmes, 2020). Monitoring mammalian viruses is very important to prevent the occurrence of virus transmission from animals to humans. Furthermore, monitoring viruses that have infected humans is important to prevent wider transmission and to identify mutations during transmission.